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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_edabi.wasp
Title produced by softwareBivariate Explorative Data Analysis
Date of computationWed, 28 Oct 2009 12:56:15 -0600
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Oct/28/t1256756259jqjarupxwgxf1ne.htm/, Retrieved Sat, 08 Nov 2025 20:51:01 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=51736, Retrieved Sat, 08 Nov 2025 20:51:01 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordspart2 model 2 Bivariate eda
Estimated Impact282

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
-   PD  [Bivariate Data Series] [part 2 model 1] [2009-10-28 18:19:40] [616e2df490b611f6cb7080068870ecbd]
- RMP     [Bivariate Explorative Data Analysis] [part 2 model 1 bi...] [2009-10-28 18:28:17] [616e2df490b611f6cb7080068870ecbd]
-    D        [Bivariate Explorative Data Analysis] [part2 model 2 Biv...] [2009-10-28 18:56:15] [88e98f4c87ea17c4967db8279bda8533] [Current]
-    D          [Bivariate Explorative Data Analysis] [part 2 model 3 bi...] [2009-10-28 19:22:48] [616e2df490b611f6cb7080068870ecbd]
- RMPD          [Kendall tau Rank Correlation] [ws4 part 3] [2009-10-28 19:54:27] [616e2df490b611f6cb7080068870ecbd]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
68
61
44
22
13
15
43
55
57
47
40
41
47
46
45
39
38
29
36
35
35
38
37
39
41
43
42
41
38
32
23
14
15
20
21
23
24
22
19
19
17
11
13
12
10
8
7
8
9
8
7
5
2
3
6
6
4
2
1
1
1
4
4
3
1
1
1
1
Dataseries Y:
Download CSV
Histogram 
60
59
50
25
17
27
60
61
60
56
44
43
48
49
47
39
29
34
48
49
48
44
39
40
41
40
38
33
32
31
37
37
36
35
30
29
31
30
29
29
28
23
24
18
10
14
13
13
12
8
4
2
1
1
9
13
10
6
3
4
6
6
5
3
1
1
1
1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51736&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51736&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51736&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Model: Y[t] = c + b X[t] + e[t]
c5.23409836416252
b0.958465330285955

\begin{tabular}{lllllllll}
\hline
Model: Y[t] = c + b X[t] + e[t] \tabularnewline
c & 5.23409836416252 \tabularnewline
b & 0.958465330285955 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51736&T=1

[TABLE]
[ROW][C]Model: Y[t] = c + b X[t] + e[t][/C][/ROW]
[ROW][C]c[/C][C]5.23409836416252[/C][/ROW]
[ROW][C]b[/C][C]0.958465330285955[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51736&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51736&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Model: Y[t] = c + b X[t] + e[t]
c5.23409836416252
b0.958465330285955






Descriptive Statistics about e[t]
# observations68
minimum-12.6557809150288
Q1-4.8395190018791
median-0.922588341307187
mean1.29619558550561e-16
Q33.20764744496905
maximum18.3473870118341

\begin{tabular}{lllllllll}
\hline
Descriptive Statistics about e[t] \tabularnewline
# observations & 68 \tabularnewline
minimum & -12.6557809150288 \tabularnewline
Q1 & -4.8395190018791 \tabularnewline
median & -0.922588341307187 \tabularnewline
mean & 1.29619558550561e-16 \tabularnewline
Q3 & 3.20764744496905 \tabularnewline
maximum & 18.3473870118341 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=51736&T=2

[TABLE]
[ROW][C]Descriptive Statistics about e[t][/C][/ROW]
[ROW][C]# observations[/C][C]68[/C][/ROW]
[ROW][C]minimum[/C][C]-12.6557809150288[/C][/ROW]
[ROW][C]Q1[/C][C]-4.8395190018791[/C][/ROW]
[ROW][C]median[/C][C]-0.922588341307187[/C][/ROW]
[ROW][C]mean[/C][C]1.29619558550561e-16[/C][/ROW]
[ROW][C]Q3[/C][C]3.20764744496905[/C][/ROW]
[ROW][C]maximum[/C][C]18.3473870118341[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=51736&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=51736&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Descriptive Statistics about e[t]
# observations68
minimum-12.6557809150288
Q1-4.8395190018791
median-0.922588341307187
mean1.29619558550561e-16
Q33.20764744496905
maximum18.3473870118341


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 0 ; par2 = 36 ;
Parameters (R input):
par1 = 0 ; par2 = 36 ;
R code (references can be found in the software module):
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
x <- as.ts(x)
y <- as.ts(y)
mylm <- lm(y~x)
cbind(mylm$resid)
library(lattice)
bitmap(file='pic1.png')
plot(y,type='l',main='Run Sequence Plot of Y[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic1a.png')
plot(x,type='l',main='Run Sequence Plot of X[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic1b.png')
plot(x,y,main='Scatter Plot',xlab='X[t]',ylab='Y[t]')
grid()
dev.off()
bitmap(file='pic1c.png')
plot(mylm$resid,type='l',main='Run Sequence Plot of e[t]',xlab='time or index',ylab='value')
grid()
dev.off()
bitmap(file='pic2.png')
hist(mylm$resid,main='Histogram of e[t]')
dev.off()
bitmap(file='pic3.png')
if (par1 > 0)
{
densityplot(~mylm$resid,col='black',main=paste('Density Plot of e[t]   bw = ',par1),bw=par1)
} else {
densityplot(~mylm$resid,col='black',main='Density Plot of e[t]')
}
dev.off()
bitmap(file='pic4.png')
qqnorm(mylm$resid,main='QQ plot of e[t]')
qqline(mylm$resid)
grid()
dev.off()
if (par2 > 0)
{
bitmap(file='pic5.png')
acf(mylm$resid,lag.max=par2,main='Residual Autocorrelation Function')
grid()
dev.off()
}
summary(x)
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Model: Y[t] = c + b X[t] + e[t]',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'c',1,TRUE)
a<-table.element(a,mylm$coeff[[1]])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'b',1,TRUE)
a<-table.element(a,mylm$coeff[[2]])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Descriptive Statistics about e[t]',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'# observations',header=TRUE)
a<-table.element(a,length(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'minimum',header=TRUE)
a<-table.element(a,min(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q1',header=TRUE)
a<-table.element(a,quantile(mylm$resid,0.25))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'median',header=TRUE)
a<-table.element(a,median(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,mean(mylm$resid))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Q3',header=TRUE)
a<-table.element(a,quantile(mylm$resid,0.75))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'maximum',header=TRUE)
a<-table.element(a,max(mylm$resid))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')